ACME:用于全面重建细胞膜的自动细胞形态提取器。

ACME: automated cell morphology extractor for comprehensive reconstruction of cell membranes.

机构信息

Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America.

出版信息

PLoS Comput Biol. 2012;8(12):e1002780. doi: 10.1371/journal.pcbi.1002780. Epub 2012 Dec 6.

Abstract

The quantification of cell shape, cell migration, and cell rearrangements is important for addressing classical questions in developmental biology such as patterning and tissue morphogenesis. Time-lapse microscopic imaging of transgenic embryos expressing fluorescent reporters is the method of choice for tracking morphogenetic changes and establishing cell lineages and fate maps in vivo. However, the manual steps involved in curating thousands of putative cell segmentations have been a major bottleneck in the application of these technologies especially for cell membranes. Segmentation of cell membranes while more difficult than nuclear segmentation is necessary for quantifying the relations between changes in cell morphology and morphogenesis. We present a novel and fully automated method to first reconstruct membrane signals and then segment out cells from 3D membrane images even in dense tissues. The approach has three stages: 1) detection of local membrane planes, 2) voting to fill structural gaps, and 3) region segmentation. We demonstrate the superior performance of the algorithms quantitatively on time-lapse confocal and two-photon images of zebrafish neuroectoderm and paraxial mesoderm by comparing its results with those derived from human inspection. We also compared with synthetic microscopic images generated by simulating the process of imaging with fluorescent reporters under varying conditions of noise. Both the over-segmentation and under-segmentation percentages of our method are around 5%. The volume overlap of individual cells, compared to expert manual segmentation, is consistently over 84%. By using our software (ACME) to study somite formation, we were able to segment touching cells with high accuracy and reliably quantify changes in morphogenetic parameters such as cell shape and size, and the arrangement of epithelial and mesenchymal cells. Our software has been developed and tested on Windows, Mac, and Linux platforms and is available publicly under an open source BSD license (https://github.com/krm15/ACME).

摘要

细胞形状、细胞迁移和细胞重排的定量分析对于解决发育生物学中的经典问题(如模式形成和组织形态发生)非常重要。通过表达荧光报告基因的转基因胚胎的延时显微镜成像,是追踪形态发生变化以及在体内建立细胞谱系和命运图谱的首选方法。然而,在这些技术的应用中,特别是在细胞膜方面,涉及到对数千个可能的细胞分割进行手工编辑,这一直是一个主要的瓶颈。细胞膜的分割比核分割更难,但对于量化细胞形态变化与形态发生之间的关系是必要的。我们提出了一种新颖的、全自动的方法,可以首先重建细胞膜信号,然后从 3D 细胞膜图像中分割出细胞,即使在密集的组织中也是如此。该方法有三个阶段:1)检测局部细胞膜平面,2)投票填补结构间隙,3)区域分割。我们通过将其结果与人工检查的结果进行比较,在斑马鱼神经外胚层和轴旁中胚层的延时共聚焦和双光子图像上定量地证明了算法的优越性能。我们还与通过模拟在不同噪声条件下使用荧光报告基因成像过程生成的合成微观图像进行了比较。我们的方法的过度分割和欠分割百分比都在 5%左右。与专家手动分割相比,单个细胞的体积重叠率始终超过 84%。通过使用我们的软件(ACME)来研究体节形成,我们能够以高精度分割接触的细胞,并可靠地量化形态发生参数的变化,如细胞形状和大小,以及上皮和间充质细胞的排列。我们的软件已经在 Windows、Mac 和 Linux 平台上开发和测试,并在开源 BSD 许可证下(https://github.com/krm15/ACME)公开提供。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2ee/3516542/cd7b6651d638/pcbi.1002780.g001.jpg

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